


"""simple docstring"""
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					['''image_processor''', '''tokenizer''']
   __UpperCAmelCase							       =					'''BlipImageProcessor'''
   __UpperCAmelCase							       =					('''BertTokenizer''', '''BertTokenizerFast''')





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		False
      super().__init__(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		self.image_processor





   def __call__(							self      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 0      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  BatchEncoding:
      if images is None and text is None:
         raise ValueError("""You have to specify either images or text.""")

      # Get only text
      if images is None:
         _lowerCamelCase	:					Any   =		self.tokenizer
         _lowerCamelCase	:					str   =		self.tokenizer(
             text=SCREAMING_SNAKE_CASE      ,   add_special_tokens=SCREAMING_SNAKE_CASE      ,   padding=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   max_length=SCREAMING_SNAKE_CASE      ,   stride=SCREAMING_SNAKE_CASE      ,   pad_to_multiple_of=SCREAMING_SNAKE_CASE      ,   return_attention_mask=SCREAMING_SNAKE_CASE      ,   return_overflowing_tokens=SCREAMING_SNAKE_CASE      ,   return_special_tokens_mask=SCREAMING_SNAKE_CASE      ,   return_offsets_mapping=SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE      ,   return_length=SCREAMING_SNAKE_CASE      ,   verbose=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
         return text_encoding

      # add pixel_values
      _lowerCamelCase	:					Dict   =		self.image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE)

      if text is not None:
         _lowerCamelCase	:					Optional[Any]   =		self.tokenizer(
             text=SCREAMING_SNAKE_CASE      ,   add_special_tokens=SCREAMING_SNAKE_CASE      ,   padding=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   max_length=SCREAMING_SNAKE_CASE      ,   stride=SCREAMING_SNAKE_CASE      ,   pad_to_multiple_of=SCREAMING_SNAKE_CASE      ,   return_attention_mask=SCREAMING_SNAKE_CASE      ,   return_overflowing_tokens=SCREAMING_SNAKE_CASE      ,   return_special_tokens_mask=SCREAMING_SNAKE_CASE      ,   return_offsets_mapping=SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE      ,   return_length=SCREAMING_SNAKE_CASE      ,   verbose=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
      else:
         _lowerCamelCase	:					List[Any]   =		None

      if text_encoding is not None:
         encoding_image_processor.update(SCREAMING_SNAKE_CASE)

      return encoding_image_processor





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      return self.tokenizer.decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					str   =		self.tokenizer.model_input_names
      _lowerCamelCase	:					Optional[Any]   =		self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase_  :							Dict             =    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_  :							List[Any]             =    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_  :							List[str]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




